Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 4826-4840 |
| Seitenumfang | 15 |
| Fachzeitschrift | IEEE Transactions on Medical Imaging |
| Jahrgang | 44 |
| Ausgabenummer | 12 |
| Frühes Online-Datum | 25 Juni 2025 |
| Publikationsstatus | Veröffentlicht - 2 Dez. 2025 |
Abstract
Cell tracking and segmentation enable biologists to extract insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency and an inability to correctly reconstruct lineage trees. To address this issue, we introduce a novel assignment strategy consisting of two key components. First, we propose an uncertainty estimation technique for motion estimation frameworks. This method relaxes single-point motion representations into probabilistic spatial densities using problem-specific test-time augmentations. Second, we leverage these spatial densities to define a novel mitosis-aware assignment problem formulation. This formulation allows multi-hypothesis trackers to model cell divisions and resolve false associations and mitosis detections based on long-term conflicts. Our framework integrates explicit biological knowledge into assignment costs and combines it with learned representations derived from spatial densities. We evaluate our approach on nine competitive datasets and demonstrate that it substantially outperforms the current state-of-the-art on biologically inspired metrics, achieving improvements by a factor of approximately six and providing new insights into the behavior of motion estimation uncertainty.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Gesundheitsberufe (insg.)
- Radiologie- und Ultraschalltechnik
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: IEEE Transactions on Medical Imaging, Jahrgang 44, Nr. 12, 02.12.2025, S. 4826-4840.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Cell Tracking according to Biological Needs
T2 - Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty
AU - Kaiser, Timo
AU - Schier, Maximilian
AU - Rosenhahn, Bodo
N1 - Publisher Copyright: © 1982-2012 IEEE.
PY - 2025/12/2
Y1 - 2025/12/2
N2 - Cell tracking and segmentation enable biologists to extract insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency and an inability to correctly reconstruct lineage trees. To address this issue, we introduce a novel assignment strategy consisting of two key components. First, we propose an uncertainty estimation technique for motion estimation frameworks. This method relaxes single-point motion representations into probabilistic spatial densities using problem-specific test-time augmentations. Second, we leverage these spatial densities to define a novel mitosis-aware assignment problem formulation. This formulation allows multi-hypothesis trackers to model cell divisions and resolve false associations and mitosis detections based on long-term conflicts. Our framework integrates explicit biological knowledge into assignment costs and combines it with learned representations derived from spatial densities. We evaluate our approach on nine competitive datasets and demonstrate that it substantially outperforms the current state-of-the-art on biologically inspired metrics, achieving improvements by a factor of approximately six and providing new insights into the behavior of motion estimation uncertainty.
AB - Cell tracking and segmentation enable biologists to extract insights from large-scale microscopy time-lapse data. Driven by local accuracy metrics, current tracking approaches often suffer from a lack of long-term consistency and an inability to correctly reconstruct lineage trees. To address this issue, we introduce a novel assignment strategy consisting of two key components. First, we propose an uncertainty estimation technique for motion estimation frameworks. This method relaxes single-point motion representations into probabilistic spatial densities using problem-specific test-time augmentations. Second, we leverage these spatial densities to define a novel mitosis-aware assignment problem formulation. This formulation allows multi-hypothesis trackers to model cell divisions and resolve false associations and mitosis detections based on long-term conflicts. Our framework integrates explicit biological knowledge into assignment costs and combines it with learned representations derived from spatial densities. We evaluate our approach on nine competitive datasets and demonstrate that it substantially outperforms the current state-of-the-art on biologically inspired metrics, achieving improvements by a factor of approximately six and providing new insights into the behavior of motion estimation uncertainty.
KW - Aleatoric Uncertainty
KW - Cell Segmentation
KW - Cell Tracking
KW - Multi-Hypothesis Tracking
UR - http://www.scopus.com/inward/record.url?scp=105009656355&partnerID=8YFLogxK
U2 - 10.1109/TMI.2025.3583148
DO - 10.1109/TMI.2025.3583148
M3 - Article
AN - SCOPUS:105009656355
VL - 44
SP - 4826
EP - 4840
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 12
ER -